Comparison of Techniques for Visualising Fire BehaviourJ Black, C Arrowsmith, M Black and W CartwrightResearch Article
Comparison of Techniques for Visualising Fire Behaviour
Julian Black, Colin Arrowsmith, Michael Black and William Cartwright
School of Mathematical and Geospatial Sciences RMIT University
Keywords:
Fire modelling, geographic visualisation
Abstract
During every Australian summer fires are common in the south-eastern region of thecontinent. The combined forces of climate, topography and vegetation make Victoriain particular, one of the most fire prone regions on earth (DSE 2003). Throughoutits history, Victoria has seen a number of devastating bushfires, including BlackFriday 1939, Ash Wednesday 1983, and more recently in the northeast of the Statein 2003. The loss of life combined with the damage caused to land and propertyresults in a heavy cost to the community. In Victoria, two of the organizationsinvolved in fire management are the Victorian Department of Sustainability andEnvironment (DSE) and the Country Fire Authority (CFA). Both use fire ‘meters’ todetermine potential fire behaviour given certain conditions. Values for temperature,wind speed, fuel load and vegetation type are input and a numerical estimate of firedanger given. There are a number of different meters used for different locations andenvironmental types. The most common meter used in Victoria is the McArthurMeter (CSIR0 2001b). The output data from this meter is numerical, and providesno spatial representation of fire danger. This paper looks at a variety of techniquesused to visualise the numerical output from the McArthur Forest Fire Danger Meter.The article outlines the different models used by fire managers to simulate a firesituation, to assess future scenarios and for decision making involving firemanagement. Particular emphasis is placed on the McArthur Forest Fire DangerMeter as this is commonly used by fire departments in Australia. The article thenfocuses on geographical visualisation and a number of techniques employed to conveyspatial information are discussed. The article then goes on to describe the firesimulation prototypes created for a study, a visualisation proof-of-concept productfor organizations involved in managing bushfires in Australia. Finally, results fromthe evaluation of the prototype are presented.
Address for correspondence:
William Cartwright, School of Mathematical and Geospatial Sciences,RMIT University, GPO Box 2476V, Melbourne, VIC 3001, Australia. E-mail: [email protected]
During every Australian summer, bushfire danger is constantly present – affecting thelives of people who live in rural and urban fringe areas. This danger is particularlypresent in the south east of the continent where a combination of hot and dry weatherin the summer and autumn months leaves the region most susceptible to fire. Along withweather conditions, the combined forces of vegetation and terrain make this region oneof the most fire prone on earth (DSE 2003). The loss of lives and property destructioncaused by bushfires in Victoria alone has been demonstrated on many occasions, withmajor bushfires occurring in 1939, 1983 and more recently, 2003.
The main goal of wildland fire management is to reduce the negative impacts of fireto society (Bachmann and Allgower 2002). Currently, fire managers can use a numberof fire behaviour tools ranging from McArthur’s circular slide meter to more complexspatial information systems, which depict fire behaviour. These fire behaviour tools areused for the purpose of assessing the characteristics of a current fire, projecting thefuture development of a fire, and evaluating alternative control strategies (Gould 2003).Current models depict different characteristics of fire behaviour including the extent ofa fire front, rate of spread, intensity, flame height and spotting distance. The majorityof research into fire behaviour models focuses on the accuracy of the input data givencertain conditions, vegetation types and fuel loads. This is necessary so that models canbecome more reliable and give fire managers a far more accurate tool for predicting firebehaviour.
In addition to the accuracy of the outputs from fire behaviour models, the way inwhich the outputs are presented is also important. There are a number of differentoptions available to view spatial information and selecting the right one or combinationis essential for effective communication. This article looks at a number of techniques forvisualizing spatial data, specifically fire behaviour.
The article begins by looking at the factors that affect fire behaviour. Factorsdescribing fuel, terrain and weather are necessary in predicting fire behaviour. Thearticle then outlines the different meters and models used by fire managers to simulatea current fire situation, to assess future scenarios and for decision making involving firemanagement. The limitations of the models in terms of accuracy are discussed as well astheir development into the future. Particular emphasis is placed on the McArthur ForestFire Danger Meter, as this is commonly used by the relevant fire departments in Australia.An outline of the meter operation, the input data required, the meter output describing firecharacteristics and its use by the relevant fire authorities is provided. Its limitations in termsof accuracy also are documented with examples from studies into fire behaviour. The articlethen focuses on geographical visualisation of spatial information related to bushfires.An analysis of a visualisation study by Verbree et al. (1999) focuses on the techniquesemployed to convey spatial information to the user/viewer. The article then goes on todescribe the fire simulation prototypes created for the study and the results from evaluationby the people and organizations involved in bushfire management in Victoria, Australia.
2 Fire Behaviour
There are a number of factors that contribute to bushfire characteristics: a fire’s inten-sity; flame height and rate of spread. These are of interest to fire departments who are
Comparison of Techniques for Visualising Fire Behaviour
in charge of fire prevention and suppression. The rate at which fire spreads is deter-mined by three factors: fuel; weather; and topography (NSW Fire Brigades 2004). Ifdata on all these factors are available the characteristics of the fire can be predicted,allowing plans and procedures to be put in place for fire fighting. While topography canbe determined from contour maps or Digital Terrain Models (DTMs), obtaining datafor fuel and weather is more difficult and time consuming. Weather can vary over timeand area, and fuel can change in type, continuity and density across a landscape.Both weather and fuel are the cause of the greatest uncertainty in predicting bushfirebehaviour (Gould 2003).
2.1 Fire Behaviour Models
Fire behaviour models are used by fire departments to assess a current fire situation, toassess future scenarios and for decision making involving fire management (Gould2003). The output from these models generally provides information about fire inten-sity, rate of spread, extent and flame height. This information is important for decisionmaking in a fire fighting situation for potential fire threat, minimising fire damage toparks, buildings and towns, and public or fire fighter health and safety (Gould 2003).A broad range of fire models have been developed, and the appropriateness of a modelfor a fire situation is determined by the model’s complexity, available input data, environ-mental conditions, computational resources and time availability (Gould 2003). Cary(1999) identified a number of fire models ranging from simple models that predict afire’s forward rate of spread, to computer simulation models and fire threat analysis.
The simplest of the fire behaviour models are the slide rules like the McArthurForest Fire Danger Meter (Gould 2003). Input data for these models include weatherconditions, fuel and topography. The models then predict certain aspects of fire behavi-our including the rate of spread, intensity and flame height. Models like the McArthurForest Meter have been created after experimentation with fire and observing unplannedwild fires (Cary 1999). A similar fire behaviour model for fire conditions in the UnitedStates is the Rothermel Model (Bachmann and Allgower 2002). It was developed by theUnited States Department of Agriculture (USDA) in 1972 for calculating the behaviourof a surface fire. Rothermel’s model produces a number of outputs which describe firebehaviour including rate of fire spread, the direction of maximum spread, fire lineintensity, heat release per unit area and flame length (Bachmann and Allgower 2002).Seventeen input variables are necessary, including eight that describe fuel type, five forfuel moisture and two for both wind and terrain. This model is still widely used, howeverin Australia the preferred fire behaviour tool is the McArthur meter.
2.2 McArthur Forest Fire Danger Meter
The principal factors contributing to forest fire behaviour were first formalised byAustralian forest researcher A.G. McArthur in the form of a circular slide rule knownas the McArthur Meter (Chapman 1994). McArthur’s fire danger rating system wasdevised after observing 800 experimental fires, burning for between 15 and 60 minutes(McArthur 1967). Separate fire meters for grasslands and forests, predict fire behaviourgiven certain conditions. The McArthur Forest Fire Danger Meter (FFDM) (see Figure 1)requires a number of inputs including fuel load, wind speed, temperature, humidity,slope and recent rainfall. From the inputted data the meter makes predictions for a fire’s
rate of spread, flame height, spotting distance (the distance flames are carried forwardof the fire front, starting new fires) and a value for the Fire Danger Index (FDI).
The FDI ranges from 1 to 100 and is related to a fire’s characteristics and thelikelihood of suppression. An index of 1 indicates that a fire will burn slowly or not atall, resulting in easy control of the fire. An index of 100 indicates that a fire will burnat a hot temperature and spread to an extent where control is virtually impossible(McArthur 1967). FDI values are used to classify fire danger into; low, moderate, high,very high and extreme. These classes have been widely used by fire managers in Australiafor fire danger forecasting, to set preparedness levels and for issuing public warnings onfire danger (Cary 1999).
Figure 1 McArthur Forest Fire Danger Meter (CSIRO 2001c)
Comparison of Techniques for Visualising Fire Behaviour
Cary (1999) says McArthur’s Forest Fire Danger Meter is limited in making firebehaviour predictions to a specific area and time. Accurate prediction of fire behaviourfor a broader area, over different time intervals, requires regular adjustments to theinput data. This is in recognition of the changes in terrain, fuel and climatic conditionsthat result in varied fire behaviour (Cary 1999). For this reason, the McArthur meter isused sparingly during large-scale bushfire situations due to the regular adjustmentsneeded for accurate fire predictions. In 2001, the CSIRO surveyed fire managers on theirusage of the McArthur Meter. The survey found that fire managers widely used themeter, in particular calculating the FDI on a regular basis. However, it was used infre-quently in calculating a fire’s forward rate of spread in a wildfire situation, which wasits original purpose (CSIRO 2001a, c). Fire managers felt that their own experience wassufficient and they indicated that they would prefer to use actual fire spread informationrather than an estimate. The survey also revealed that fire managers found it difficult toinclude the effect of topography with the McArthur Meter (CSIRO 2001a, c). TheCSIRO survey found that “The complexity of calculations and the sheer number ofcalculations required to incorporate the effects of topography, the changing fuel typesand the changing meteorological conditions mean that a fire controller must expend alot of vital time to utilise the McArthur meters effectively” (CSIRO 2001a, c).
3 Computer-Based Fire Simulation Modelling
A computer simulation model of fire behaviour was created by CSIRO’s BushfireBehaviour and Management group. Sirofire, which is a PC-based bushfire simulator,operates in a similar manner to the bushfire meters described above, however fire spreadis shown graphically. Input data for temperature, wind speed and direction, relativehumidity, fuel load and conditions, grass curing and slope are required for the firesimulation to be performed (CSIRO 2001a, c). The simulation predicts the likely spreadof fire in all directions and depicts its perimeter at certain time intervals (Coleman andSullivan 1996). The extent of the fire is displayed on a map and changes over time,giving the fire manager a predictive visual tool for fire behaviour (CSIRO 2001a, c). Thefire spread is influenced by terrain, which is derived from a digital elevation model, andfuel type, which is stored in the geographic database (Cary 1999). Once the fire managerinputs fuel load and weather conditions, the simulation can then be performed. Fromthe simulation fire managers are able to view the movement and extent of the fire,identify natural or constructed features under threat and coordinate fire suppressionstrategies. Sirofire is only one example of a fire simulator. Other models created inAustralia include FireMaster and Firescape. Figure 2 shows the user interface for the firesimulator FireMaster. Users input data on the left of the screen for wind, temperature,humidity, drought factor and fire ignition (co-ordinates). The movement of the fireacross the landscape is then shown on the right.
4 Threat Analysis
Wildfire Threat Analysis (WTA) not only concerns fire behaviour (described as ‘hazard’ inWTA), but also the risk of ignition and recognises that there are natural and man-madefeatures in the environment (Cary 1999). WTA requires three components (Government
of South Australia 2004); one for fire hazard, in which an investigation into fire behavi-our over the landscape, given certain fuel, weather conditions and changes in slope isperformed. The second component is a risk assessment, where analyses of the possibilityof fire ignition are conducted. This is influenced by what the environment is used for,the proximity to roads, residential areas (Cary 1999), and other factors which affect theprobability of a fire starting in the first place. The third component is a value assessmentof all natural and constructed assets in the environment. In this component differentland uses and features, such as residential properties, sheds, reservoirs, farmland andforests are identified, and values are assigned according to their material worth (Cary1999). Areas where higher natural and constructed values are present, are the primaryconcern for fire prevention and suppression. For fire threat to be evaluated for a particulararea all three components; hazard, risk and value are combined. An overall fire threatmap identifies and classifies areas into low, moderate, high, very high and extreme firethreat. These categories are assigned to an area depending on fire behaviour, risk ofignition and the values placed on natural and constructed values (Government of SouthAustralia 2004). The method described above was used by the South Australian Govern-ment in identifying the threat of wildfires in national parks and is just one example ofconducting a WTA. As there are no set criteria for constructing a WTA, different resultsmay be found depending on which department or organisation conducts the analysis(Cary 1999).
Figure 2 Graphic User Interface for the FireMaster simulation model (Eklund 2001, p. 367)
Comparison of Techniques for Visualising Fire Behaviour
As with all models representing real life events, bushfire behaviour models cannot berelied on to give an accurate representation of the real life situation. The Wildland FireOperations Group (2004) states that the results from the models do not always agreewith the observed fire behaviour for the following reasons:
• The model may not be applicable to the situation;• The model’s inherent accuracy may be at fault; and• The data used in the model may be inaccurate.
Most research into fire behaviour modelling has focussed on the accuracy of the inputdata used for the various models. Heywood et al. (2002) points out that the accuracyof the input data is important, as models representing real world events are only asaccurate as the data used to construct them. When investigating errors present in firebehaviour modelling Bachmann and Allgower (2002, p. 122) state that in “fire behaviourmodelling much more emphasis should be put on the assessment of input data.”
Unlike topographic data, it is difficult to gather accurate data on weather and fuel,which are the main cause of error in fire behaviour models (Gould 2003). Many modelsrequire input data for fuel load. However, fuel load is only one of the many character-istics that describe fuel. Fuel has many other characteristics that could be input into amodel, including continuity, type, density, moisture content and percentage of deadmaterial (Gould 2003). The Wildland Fire Operations Research Group (2004) state thatthe problem with describing fuel in a fire behaviour model is the assumption that thefuel bed is continuous and uniform across an area and there is no distinction madebetween surface, elevated or crown fuel. On this, the Wildland Fire Operations ResearchGroup (2004) claim that “the more the real fuel situation departs from this ideal (uni-form and continuous), the more erratic the prediction will be when compared to realfire behavior.” Due to time and cost constraints, gathering accurate data on all elementsfor fuel is difficult. Therefore, most fire models only require input data for fuel load overan area, which results in only one of the fuel characteristics entered into the model.
Another source of error in fire behaviour models is caused by weather. As winddirection and speed is constantly changing along with relative humidity, models need tobe updated regularly. At present research is being conducted to improve the accuracy ofwind data by creating terrain models, combined with satellite weather data and groundstations so that more accurate data can be used in fire behaviour models (Gould 2003).
4.2 Future Development
While fire behaviour models, such as the McArthur Forest Fire Danger Meter are stillin use and provide fire managers with a guide to fire behaviour, new possibilities existby visualising fire characteristics through fire simulation models. These models, devel-oped for computer screen output, display fire behaviour graphically. Some examples ofthese include FireMaster, Firescape and Sirofire. Like the fire models created in the past,future fire behaviour models will depend on accurate input data as explained by Gould(2003, p. 63) “the next generation of fire behaviour models needs to have the ability topredict extreme fire behaviour from readily available weather, fuel and topographicdata.” Constantly updating data is necessary for models to show current fire behaviourconditions. For this reason, Gould (2003) sees that it is necessary for future fire models
to be capable of receiving and including current weather conditions from a variety ofsources, like weather stations and global positioning systems.
5 Fire Visualisations
As the accuracy of models and input data is important, so too is the way information ispresented to an end user. It is evident that a number of possibilities exist when visualisingdata. Certain visualisations may suit the needs of different people or open up alternativeareas for data exploration. With multiple presentations of the same data, users are ableview information through a number of ‘windows’ allowing for greater analysis of thedata. Taking this into account the following research question was developed:
Fire behaviour can be depicted graphically in a variety of different ways. Whatis the most effective way of displaying fire behaviour, so that the appropriateorganizations and the people affected gain accurate and easy to understandinformation?
To answer this question three visualisations were created based on a study by Verbreeet al. (1999) which outlined a number of visualisation techniques to depict constructionwork in the Netherlands. They discussed the development of a 3D GIS and VirtualReality system. The purpose of the system was to give the user a number of visualisationsto gain a greater understanding of large-scale infrastructure projects in the Netherlands.The system gave the user three views to proposed buildings that assisted decision makersin the design, development and presentation of the building project. The three differentvisualisations created for the system were a plan view, model view and world view. Theplan view was created in a GIS and was similar in appearance to a traditional map.However, a greater amount of geographical information was available beyond what wasvisible on the screen. A user could view an object’s attributes, select and query informa-tion, and perform a number of analytical operations. The model view was a symbolic,3D representation of buildings and the environment and gave the user a bird’s eye viewof the scene. This visualisation lacked detail and only provided a general representationof the 3D objects within the scene. It was used for depicting size, dimension, the relationbetween objects and their general arrangement. Analysis preformed at this level includedthe calculation of volume, distances, shadowing and line-of-sight. The world view was3D and photo realistic, giving the user a virtual reality view of the scene. It allowed theuser to see the building project from within any position in the environment. The scenewas continually updated so the user was able to ‘walk through’ and view buildings fromdifferent perspectives.
The three views, plan, model and world, offered the user a choice of their preferredmethod of visualisation. Each view, while depicting the same environment, had a differ-ent purpose in the analysis and visualisation of the scene. By having more than onemethod of viewing data the user is able to gain a better understanding of what is beingvisualised. This can be attributed to the different visualisations offering a number of‘windows’ to view data, allowing the user to choose what is most suitable for them. Asimilar situation exists where an architect creates a plan view of a proposed building,then sketches the building from a certain angle, then finally constructs a 3D model ofthe building in its environment. The system outlined by Verbree et al. (1999) allowedfor all these views to be seen and analysed with greater efficiency.
Comparison of Techniques for Visualising Fire Behaviour
For this study, a number of visualisations were created, all depicting fire spreadover the study area. To visualise fire spread, data were gathered and input into theMcArthur Forest Fire Danger Meter. The meter requires input data for fuel load,temperature, relative humidity, wind speed, drought factor and ground slope. Datawere collected for fuel load using the method outlined by McCarthy et al. (1998) in theVictoria Department of Natural Resources and Environment, Fuel Hazard Guide. Valuesfor temperature, relative humidity and wind speed were gathered and reflect conditionsexpected in the summer months where fire danger is at its peak. Ground slope wasdetermined through GIS analysis. These data were then used in the McArthur ForestFire Danger Meter that gave the expected rate of spread of the fire. While care wastaken in gathering data, the meter output was only used as a guide to how quickly thefire might travel across the area. The purpose of this was to assist in the animationprocess, so the visualisations would approximate the fire propagation that might occurin the study area. The data used to calculate fire spread was the same for each of thevisualisations as was the point of fire ignition. The data used to construct the visualisationswas the 1:25,000-scale Land Victoria Topographic Dataset. A satellite image of the areawas also used in creating visualisation 3.
All visualisations were tested using participants who were most likely to use firesimulations. The test participants were from the Victoria Department of Sustainabilityand Environment (DSE), who would find visualisations of fire spread useful to assess acurrent fire situation, to assess future scenarios and for decision making involving firemanagement (Gould 2003). The testing procedure required the participant to view anumber of visualisations of fire behaviour. They were asked to find certain informationthat each view was able to show. Through the participant viewing these visualisationsand seeking information they became familiar with what each view had to offer. Anevaluation of each view was then completed by the participants outlining the respectivestrengths and weaknesses of each visual representation. Feedback from the participantsindicated user preferences, and helped in making recommendations for effectivelyvisualising fire spread.
The software used to create the three fire visualisations included ArcGIS, Corel-Draw and Macromedia Flash. ArcGIS was used for analysing spatial data and preparingit for export to Macromedia Flash where the animations were created. In CorelDRAWgraphics were created for the timeline and input data column. The data column was thesame for the three visualizations as they are not fully working fire simulation models(users could not enter their own data).
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Visualisation 1 – Digital Topographic Map (Figure 3).
A computer screen display ofthe area, similar to what is viewed in a traditional topographic map. The 2D com-puter screen displays the geographical features and roads of the area. Fire is viewedspreading across the study area using the user controls.
A digital topographic map was imported into Macromedia Flash. Fire spread datafrom the McArthur Forest Fire Danger Meter was used in creating the animation. Theanimation showed fire spread over the study area for a five hour time span. The firespread graphic was manipulated in Macromedia Flash with the use of shape hints andguides to represent how a fire might propagate within the study area. A timeline wasincluded into the graphic user interface to indicate the progress of the fire. Users wereable to control the simulation with ‘stop’, ‘start’ and ‘reset’ buttons located below thesimulation window. In CorelDraw, a data input panel was created and imported to
Flash to give the impression that it was a fully working fire simulation model. Thiswas to show the user the circumstances under which the visualisations would be used.As it was not a fully working model users were not able to enter their own input data.
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Visualisation 2 – Digital Elevation Model (Figure 4).
A simple, abstract, 3D model ofthe scene showing the topography of the area with the same natural and human-madefeatures shown in visualisation 1. This view gives the user a view to the terrain of thearea with fire spreading across the landscape.
In ArcMap a height elevation raster was created from the Land Victoria digitaltopographic data. The raster was then viewed in ArcScene where the data’s 3Dattributes could be viewed. A colour scheme was given to the elevation model accord-ing to height, with roads and hydro features overlaid. The scene was then exportedinto Macromedia Flash where the fire spread was created over the study area. Thesame fire spread data from the McArthur Fire Danger Meter used in view 1 wasimplemented. The fire spread graphic was manipulated with the use of shape hintsand guides to represent how a fire would react within the study area. A timeline wasincluded into the graphic user interface to indicate the progress of the fire. As withvisualisation 1 users were able to control the simulation with ‘stop’, ‘start’ and ‘reset’buttons located below the simulation window. The same graphic user interfacedescribed in visualisation 1 was used in visualisation 2.
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Visualisation 3 – Digital Elevation Model with Satellite Image Overlay (Figure 5).
A3D view of the area with greater realism than visualisation 3. A satellite image isdraped over a Digital Elevation Model of the study area.
Figure 3 Visualisation 1 – Digital Topographic Map
Comparison of Techniques for Visualising Fire Behaviour
Visualisation 3 uses the same elevation model used in visualisation 2. In ArcScenea satellite image of the study area was imported and overlaid on the elevation model.The scene was then imported into Flash where the fire spread graphic, timeline, usercontrols and graphic user interface where the same as in visualisation 2.
6 Results
The study found that each visualisation contributed to the level of information thatparticipants acquired. There was not one visualisation that could be determined as theideal visualisation, but through a combination of views, a greater understanding wasachieved. The Digital Topographic Map allowed for the calculation of distance the firehad travelled, the rate of spread and for an uninterrupted view of the fire’s perimeter.While it was preferred by two participants for viewing topographic features, its mainadvantage was being able to view fire spread across the fire area.
The Digital Elevation Model presented a 3D view of the area to the participant.From the results of the task scenario’s and questionnaires, it was clear that this viewwas beneficial for identifying topographic features of the area. Using the same data asthe Digital Topographic Map, the Digital Elevation Model enabled a more detaileddescription of the area in a time efficient manner. It was also found that the DigitalElevation Model provided participants with a better view to the effect of topography onfire spread.
Figure 4 Visualisation 2 – Digital Elevation Model
The Digital Elevation Model with the satellite image overlay provided the partici-pant with the most realistic view of the area. From the study, it was found that this viewprovided important information that the other two views did not. While the DigitalTopographic Map and the Digital Elevation Model provided elevation, road and hydroinformation, the Digital Elevation Model with the satellite image overlay allowed forvegetation cover to be viewed. This enabled the identification of plantation areas, stateforest and farmland.
The results of the study found that both of the 3D scenes would be beneficial to fireofficers who have a limited knowledge of the area. When using paper topographic maps,fire officers are able to match features on the map with their own experience, resultingin a sufficient knowledge of the region. However, fire officers and crews often are calledto adjoining fire regions or even interstate where their knowledge of the area is limited.Participants indicated that the realistic nature of the 3D visualisations and satelliteimage, would be beneficial to fire officers or crews in the successful identification offeatures in the area.
The study also found that for understanding a fire’s characteristics numerical datashould accompany the fire visualisation. The McArthur Forest Fire Danger Meter whichdescribes a fire’s characteristics (rate of spread, flame height, spotting distance) onlyuses numerical output to describe a fire. The visualisations that were created for thepurpose of this study only showed the output for fire spread visually. It was found inthe study that both the numerical data and visualisation graphics should be available.
Figure 5 Visualisation 3 – Digital Elevation Model with Satellite Image Overlay
Comparison of Techniques for Visualising Fire Behaviour
Participants noted that they could see the fire spreading but the accompanying data forrate of spread, flame height and spotting distance should be used to complement thevisual output and gain a better understanding of the fire situation.
Each visualisation offered essentially the same information; fire spread, topographyand features in the area. However, the visualisation technique used to display this infor-mation affected the type and ease of information found. The study found that there wasnot one visualisation alone that was ideal for viewing all the information that firemanagers require. Each view had strengths and weaknesses in conveying certain infor-mation and therefore all visualisations contributed to a greater understanding of the firescenario.
7 Discussion
7.1 Multiple Views of Fire Spread Allow for a Better Understanding of the Fire Scenario
The results indicate that each of the visualisations contributed to a greater understand-ing of the fire event. Each visualisation showed fire spread over the Timbertop area,however the technique used to view fire spread, determined what information could beobtained and the difficulty in obtaining that information. For example, both the DigitalTopographic Map and the Digital Elevation Model provided elevation data but it waspresented differently. Both views could be used to find the same information with someparticipants preferring to use the Digital Topographic Map based on their own mapreading experiences. However, the results indicated that when it came to identifyingtopographic features, participants were able to give a more detailed description ofthe topography of the area using the Digital Elevation Model rather than the DigitalTopographic Map.
While each view showed fire spread over time, the Digital Topographic Map wasfound to be the ideal visualisation technique to view a fire’s spread and perimeter. Thiswas due to the easier calculation of the distance fire had travelled and being able to viewthe fire perimeter.
The Digital Elevation Model with the satellite image overlay extended the numberof features that could be identified in the area. Both the Digital Topographic Map andthe Digital Elevation Model presented the same information; elevation, roads and hydrofeatures. Assets shown in these views such as roads and hydro features are necessary forfire officers to identify, however the satellite image offered an even greater amount ofinformation. Forests, plantations and farmlands could be identified which could influencedecisions made by fire managers especially in the deployment of resources.
Having multiple options to view information allows users to choose which visual-isation or combination of visualisations suits their own requirements, experience andpreferences. Therefore, future fire simulation models should allow the fire officer toview a fire’s characteristics using a visualisation technique that is both suitable to gatherthe required information and fits comfortably with a user’s preferences.
7.2 Both Numerical Data and Visual Representations of Fire Spread are Required
The McArthur Forest Fire Danger Meter uses only numerical output to describe a fire’scharacteristics (rate of spread, flame height, spotting distance). The visualisations
created for this study only showed the spreading and the extent of the fire. The studyfound that the numerical output from the McArthur Forest Fire Danger Meter shouldbe available with the visualisations. As the visualisation shows fire spread across the area,data, in numerical form describing rate of spread, flame height and spotting distanceshould be used to complement the visual output and gain a better understanding of thefire situation.
The visualisations are only a few of the many techniques used to view spatialinformation. A number of other visualisation techniques are possible and can be viewedin a variety of output devices. These techniques include Virtual Reality Systems that havethe ability to offer realistic and fully immersive environments. These virtual environmentsmay have applications in showing events associated with geography such as bushfires.With the user able to move around the near-reality landscape, a better understanding ofthe location and attributes of features such as trees, grasslands, mountain ranges, ridge-lines, valleys, creeks, rivers, buildings, etc. may be beneficial to fire departments inbushfire management. These new techniques need to be tested for what they can offerin viewing scenarios like bushfire.
7.3 Greater Realism for Users who are Less Familiar with the Area
The three visualisations used in this study provided three different levels of realism.The Digital Topographic Map could be considered to be the most abstract of the three. Thevisualisation gives clues to the user about the topography of the area through thespacing, shape and labelling of contours. It gives sufficient information to users whohave knowledge of the area, as they are able to identify features on the topographic mapand associate them with their own experience of the area to gain sufficient understandingof the topography, land cover and proximity to other features. This, in some circumstancesis all that a fire officer would require, as they have worked and are familiar with theirown fire management zone.
However, fire crews are often called into other fire management zones, or eveninterstate where their knowledge of the area is poor. The 3D scenes presented in thisstudy offered the participants greater realism of the area. The Digital Elevation Modelgave participants a more realistic representation of the area, with contours replaced bya 3D model. Participants were able to see at a glance all the features in the area, withouthaving to interpret the contours. The Digital Elevation Model with the satellite imageoverlay, offered participants the same 3D view as the Digital Elevation Model, but alsoincluded land cover information. This enabled users who had never seen the area before,a near-realistic representation of the area. One participant stated that the visualisationgave them the feeling they were actually viewing a fire event. This view would be idealfor fire officers/crews with little knowledge of the area as they are given a realisticrepresentation of the area they are working in.
8 Conclusions
This paper has addressed the application of a number of visualisation techniques fordepicting bushfire scenarios. It was found that most research into bushfire managementhas focused on the accuracy of the models and better methods of obtaining input data.This is important to ensure that the data used in the models are accurate and current,
Comparison of Techniques for Visualising Fire Behaviour
giving fire managers confidence when making forecasts. As the accuracy of input dataused in the models is important, it is argued, so too is the way the data are presentedto the user. Through a comparison of visualisation techniques, which this paper has onlyexplored a few, an understanding of what is needed to better communicate spatialinformation for certain applications and user groups can be established. For bushfiremanagement appropriate visualisations will hopefully lead to a greater understanding ofbushfire behaviour, and in turn lead to more informed decisions being made by profes-sional fire fighting managers and the public alike.
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